






"""simple docstring"""





from math import factorial






class 	a		:






    def __init__(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						int:
      lowerCamelCase_       	=		real
      if isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		[1] * rank
      else:
        lowerCamelCase_       	=		rank






    def __repr__(			self	:				Dict   )	->						Union[str, Any]:
      return (
          F'''{self.real}+'''
          F'''{"+".join(str(__SCREAMING_SNAKE_CASE   )+"E"+str(n+1   )for n,dual in enumerate(self.duals   )   )}'''
      )






    def   UpperCamelCase			(			self	:				List[str]   )	->						Tuple:
      lowerCamelCase_       	=		self.duals.copy()
      while cur[-1] == 0:
        cur.pop(-1   )
      return Dual(self.real			,		__SCREAMING_SNAKE_CASE   )






    def __add__(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]   )	->						Optional[Any]:
      if not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        return Dual(self.real + other			,		self.duals   )
      lowerCamelCase_       	=		self.duals.copy()
      lowerCamelCase_       	=		other.duals.copy()
      if len(__SCREAMING_SNAKE_CASE   ) > len(__SCREAMING_SNAKE_CASE   ):
        o_dual.extend([1] * (len(__SCREAMING_SNAKE_CASE   ) - len(__SCREAMING_SNAKE_CASE   ))   )
      elif len(__SCREAMING_SNAKE_CASE   ) < len(__SCREAMING_SNAKE_CASE   ):
        s_dual.extend([1] * (len(__SCREAMING_SNAKE_CASE   ) - len(__SCREAMING_SNAKE_CASE   ))   )
      lowerCamelCase_       	=		[]
      for i in range(len(__SCREAMING_SNAKE_CASE   )   ):
        new_duals.append(s_dual[i] + o_dual[i]   )
      return Dual(self.real + other.real			,		__SCREAMING_SNAKE_CASE   )

    SCREAMING_SNAKE_CASE		:	List[Any]		=					__add__






    def __sub__(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]   )	->						Tuple:
      return self + other * -1






    def __mul__(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				int   )	->						Tuple:
      if not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		[]
        for i in self.duals:
          new_duals.append(i * other   )
        return Dual(self.real * other			,		__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		[0] * (len(self.duals   ) + len(other.duals   ) + 1)
      for i, item in enumerate(self.duals   ):
        for j, jtem in enumerate(other.duals   ):
          new_duals[i + j + 1] += item * jtem
      for k in range(len(self.duals   )   ):
        new_duals[k] += self.duals[k] * other.real
      for index in range(len(other.duals   )   ):
        new_duals[index] += other.duals[index] * self.real
      return Dual(self.real * other.real			,		__SCREAMING_SNAKE_CASE   )

    SCREAMING_SNAKE_CASE		:	str		=					__mul__






    def __truediv__(			self	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Optional[int]:
      if not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		[]
        for i in self.duals:
          new_duals.append(i / other   )
        return Dual(self.real / other			,		__SCREAMING_SNAKE_CASE   )
      raise ValueError






    def __floordiv__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[str]   )	->						Any:
      if not isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        lowerCamelCase_       	=		[]
        for i in self.duals:
          new_duals.append(i // other   )
        return Dual(self.real // other			,		__SCREAMING_SNAKE_CASE   )
      raise ValueError






    def __pow__(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						str:
      if n < 0 or isinstance(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        raise ValueError('power must be a positive integer'   )
      if n == 0:
        return 1
      if n == 1:
        return self
      lowerCamelCase_       	=		self
      for _ in range(n - 1   ):
        x *= self
      return x







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							Any:
  if not callable(_lowerCamelCase					):
    raise ValueError('differentiate() requires a function as input for func'					)
  if not isinstance(_lowerCamelCase						,							(float, int)					):
    raise ValueError('differentiate() requires a float as input for position'					)
  if not isinstance(_lowerCamelCase						,							_lowerCamelCase					):
    raise ValueError('differentiate() requires an int as input for order'					)
  lowerCamelCase_       	=		Dual(_lowerCamelCase						,							1					)
  lowerCamelCase_       	=		func(_lowerCamelCase					)
  if order == 0:
    return result.real
  return result.duals[order - 1] * factorial(_lowerCamelCase					)


if __name__ == "__main__":
  import doctest

  doctest.testmod()







  def 	lowerCamelCase__			(				_lowerCamelCase			:						Union[str, Any]					) ->							Dict:
    return y**2 * y**4






  print(differentiate(f, 9, 2))












"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					DebertaTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					True
    SCREAMING_SNAKE_CASE		:	Any		=					DebertaTokenizerFast






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_       	=		[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      lowerCamelCase_       	=		dict(zip(__SCREAMING_SNAKE_CASE			,		range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
      lowerCamelCase_       	=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      lowerCamelCase_       	=		{'unk_token': '[UNK]'}

      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
      with open(self.merges_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		'lower newer'
      return input_text, output_text






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      lowerCamelCase_       	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokens + [tokenizer.unk_token]
      lowerCamelCase_       	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		tokenizer('Hello'			,		'World'   )
      lowerCamelCase_       	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']			,		__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		'multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      lowerCamelCase_       	=		[self.tokenizer_class]
      if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

      for tokenizer_class in tokenizer_classes:
        lowerCamelCase_       	=		tokenizer_class.from_pretrained('microsoft/deberta-base'   )

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   ) for seq in encoding['input_ids']]

        # fmt: off
        lowerCamelCase_       	=		{
            'input_ids': [
                [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
            ],
            'token_type_ids': [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            'attention_mask': [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        self.assertDictEqual(encoding.data			,		__SCREAMING_SNAKE_CASE   )

        for expected, decoded in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




